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Code Issues Releases Activity

lavfi/dnn_backend_common: Return specific error codes

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Switch to returning specific error codes or DNN_GENERIC_ERROR
when an error is encountered in the common DNN backend functions.

Signed-off-by: Shubhanshu Saxena <shubhanshu.e01@gmail.com>
This commit is contained in:
Shubhanshu Saxena 2022-03-02 23:35:55 +05:30 committed by Guo Yejun
parent 515ff6b4f8
commit 1df77bab08
2 changed files with 27 additions and 30 deletions
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35
libavfilter/dnn/dnn_backend_common.c
View File

@ -47,19 +47,19 @@ int ff_check_exec_params(void *ctx, DNNBackendType backend, DNNFunctionType func
// currently, the filter does not need multiple outputs, // currently, the filter does not need multiple outputs,
// so we just pending the support until we really need it. // so we just pending the support until we really need it.
avpriv_report_missing_feature(ctx, "multiple outputs"); avpriv_report_missing_feature(ctx, "multiple outputs");
return AVERROR(EINVAL); return AVERROR(ENOSYS);
} }
return 0; return 0;
} }
DNNReturnType ff_dnn_fill_task(TaskItem *task, DNNExecBaseParams *exec_params, void *backend_model, int async, int do_ioproc) { int ff_dnn_fill_task(TaskItem *task, DNNExecBaseParams *exec_params, void *backend_model, int async, int do_ioproc) {
if (task == NULL || exec_params == NULL || backend_model == NULL) if (task == NULL || exec_params == NULL || backend_model == NULL)
return DNN_ERROR; return AVERROR(EINVAL);
if (do_ioproc != 0 && do_ioproc != 1) if (do_ioproc != 0 && do_ioproc != 1)
return DNN_ERROR; return AVERROR(EINVAL);
if (async != 0 && async != 1) if (async != 0 && async != 1)
return DNN_ERROR; return AVERROR(EINVAL);
task->do_ioproc = do_ioproc; task->do_ioproc = do_ioproc;
task->async = async; task->async = async;
@ -89,17 +89,17 @@ static void *async_thread_routine(void *args)
return DNN_ASYNC_SUCCESS; return DNN_ASYNC_SUCCESS;
} }
DNNReturnType ff_dnn_async_module_cleanup(DNNAsyncExecModule *async_module) int ff_dnn_async_module_cleanup(DNNAsyncExecModule *async_module)
{ {
void *status = 0; void *status = 0;
if (!async_module) { if (!async_module) {
return DNN_ERROR; return AVERROR(EINVAL);
} }
#if HAVE_PTHREAD_CANCEL #if HAVE_PTHREAD_CANCEL
pthread_join(async_module->thread_id, &status); pthread_join(async_module->thread_id, &status);
if (status == DNN_ASYNC_FAIL) { if (status == DNN_ASYNC_FAIL) {
av_log(NULL, AV_LOG_ERROR, "Last Inference Failed.\n"); av_log(NULL, AV_LOG_ERROR, "Last Inference Failed.\n");
return DNN_ERROR; return DNN_GENERIC_ERROR;
} }
#endif #endif
async_module->start_inference = NULL; async_module->start_inference = NULL;
@ -108,30 +108,31 @@ DNNReturnType ff_dnn_async_module_cleanup(DNNAsyncExecModule *async_module)
return DNN_SUCCESS; return DNN_SUCCESS;
} }
DNNReturnType ff_dnn_start_inference_async(void *ctx, DNNAsyncExecModule *async_module) int ff_dnn_start_inference_async(void *ctx, DNNAsyncExecModule *async_module)
{ {
int ret; int ret;
void *status = 0; void *status = 0;
if (!async_module) { if (!async_module) {
av_log(ctx, AV_LOG_ERROR, "async_module is null when starting async inference.\n"); av_log(ctx, AV_LOG_ERROR, "async_module is null when starting async inference.\n");
return DNN_ERROR; return AVERROR(EINVAL);
} }
#if HAVE_PTHREAD_CANCEL #if HAVE_PTHREAD_CANCEL
pthread_join(async_module->thread_id, &status); pthread_join(async_module->thread_id, &status);
if (status == DNN_ASYNC_FAIL) { if (status == DNN_ASYNC_FAIL) {
av_log(ctx, AV_LOG_ERROR, "Unable to start inference as previous inference failed.\n"); av_log(ctx, AV_LOG_ERROR, "Unable to start inference as previous inference failed.\n");
return DNN_ERROR; return DNN_GENERIC_ERROR;
} }
ret = pthread_create(&async_module->thread_id, NULL, async_thread_routine, async_module); ret = pthread_create(&async_module->thread_id, NULL, async_thread_routine, async_module);
if (ret != 0) { if (ret != 0) {
av_log(ctx, AV_LOG_ERROR, "Unable to start async inference.\n"); av_log(ctx, AV_LOG_ERROR, "Unable to start async inference.\n");
return DNN_ERROR; return ret;
} }
#else #else
if (async_module->start_inference(async_module->args) != DNN_SUCCESS) { ret = async_module->start_inference(async_module->args);
return DNN_ERROR; if (ret != DNN_SUCCESS) {
return ret;
} }
async_module->callback(async_module->args); async_module->callback(async_module->args);
#endif #endif
@ -158,7 +159,7 @@ DNNAsyncStatusType ff_dnn_get_result_common(Queue *task_queue, AVFrame **in, AVF
return DAST_SUCCESS; return DAST_SUCCESS;
} }
DNNReturnType ff_dnn_fill_gettingoutput_task(TaskItem *task, DNNExecBaseParams *exec_params, void *backend_model, int input_height, int input_width, void *ctx) int ff_dnn_fill_gettingoutput_task(TaskItem *task, DNNExecBaseParams *exec_params, void *backend_model, int input_height, int input_width, void *ctx)
{ {
AVFrame *in_frame = NULL; AVFrame *in_frame = NULL;
AVFrame *out_frame = NULL; AVFrame *out_frame = NULL;
@ -166,14 +167,14 @@ DNNReturnType ff_dnn_fill_gettingoutput_task(TaskItem *task, DNNExecBaseParams *
in_frame = av_frame_alloc(); in_frame = av_frame_alloc();
if (!in_frame) { if (!in_frame) {
av_log(ctx, AV_LOG_ERROR, "Failed to allocate memory for input frame\n"); av_log(ctx, AV_LOG_ERROR, "Failed to allocate memory for input frame\n");
return DNN_ERROR; return AVERROR(ENOMEM);
} }
out_frame = av_frame_alloc(); out_frame = av_frame_alloc();
if (!out_frame) { if (!out_frame) {
av_frame_free(&in_frame); av_frame_free(&in_frame);
av_log(ctx, AV_LOG_ERROR, "Failed to allocate memory for output frame\n"); av_log(ctx, AV_LOG_ERROR, "Failed to allocate memory for output frame\n");
return DNN_ERROR; return AVERROR(ENOMEM);
} }
in_frame->width = input_width; in_frame->width = input_width;

22
libavfilter/dnn/dnn_backend_common.h
View File

@ -60,7 +60,7 @@ typedef struct DNNAsyncExecModule {
* Synchronous inference function for the backend * Synchronous inference function for the backend
* with corresponding request item as the argument. * with corresponding request item as the argument.
*/ */
DNNReturnType (*start_inference)(void *request); int (*start_inference)(void *request);
/** /**
* Completion Callback for the backend. * Completion Callback for the backend.
@ -92,20 +92,18 @@ int ff_check_exec_params(void *ctx, DNNBackendType backend, DNNFunctionType func
* @param async flag for async execution. Must be 0 or 1 * @param async flag for async execution. Must be 0 or 1
* @param do_ioproc flag for IO processing. Must be 0 or 1 * @param do_ioproc flag for IO processing. Must be 0 or 1
* *
* @retval DNN_SUCCESS if successful * @returns DNN_SUCCESS if successful or error code otherwise.
* @retval DNN_ERROR if flags are invalid or any parameter is NULL
*/ */
DNNReturnType ff_dnn_fill_task(TaskItem *task, DNNExecBaseParams *exec_params, void *backend_model, int async, int do_ioproc); int ff_dnn_fill_task(TaskItem *task, DNNExecBaseParams *exec_params, void *backend_model, int async, int do_ioproc);
/** /**
* Join the Async Execution thread and set module pointers to NULL. * Join the Async Execution thread and set module pointers to NULL.
* *
* @param async_module pointer to DNNAsyncExecModule module * @param async_module pointer to DNNAsyncExecModule module
* *
* @retval DNN_SUCCESS if successful * @returns DNN_SUCCESS if successful or error code otherwise.
* @retval DNN_ERROR if async_module is NULL
*/ */
DNNReturnType ff_dnn_async_module_cleanup(DNNAsyncExecModule *async_module); int ff_dnn_async_module_cleanup(DNNAsyncExecModule *async_module);
/** /**
* Start asynchronous inference routine for the TensorFlow * Start asynchronous inference routine for the TensorFlow
@ -119,10 +117,9 @@ DNNReturnType ff_dnn_async_module_cleanup(DNNAsyncExecModule *async_module);
* @param ctx pointer to the backend context * @param ctx pointer to the backend context
* @param async_module pointer to DNNAsyncExecModule module * @param async_module pointer to DNNAsyncExecModule module
* *
* @retval DNN_SUCCESS on the start of async inference. * @returns DNN_SUCCESS on the start of async inference or error code otherwise.
* @retval DNN_ERROR in case async inference cannot be started
*/ */
DNNReturnType ff_dnn_start_inference_async(void *ctx, DNNAsyncExecModule *async_module); int ff_dnn_start_inference_async(void *ctx, DNNAsyncExecModule *async_module);
/** /**
* Extract input and output frame from the Task Queue after * Extract input and output frame from the Task Queue after
@ -149,9 +146,8 @@ DNNAsyncStatusType ff_dnn_get_result_common(Queue *task_queue, AVFrame **in, AVF
* @param input_width width of input frame * @param input_width width of input frame
* @param ctx pointer to the backend context * @param ctx pointer to the backend context
* *
* @retval DNN_SUCCESS if successful * @returns DNN_SUCCESS if successful or error code otherwise.
* @retval DNN_ERROR if allocation fails
*/ */
DNNReturnType ff_dnn_fill_gettingoutput_task(TaskItem *task, DNNExecBaseParams *exec_params, void *backend_model, int input_height, int input_width, void *ctx); int ff_dnn_fill_gettingoutput_task(TaskItem *task, DNNExecBaseParams *exec_params, void *backend_model, int input_height, int input_width, void *ctx);
#endif #endif